Literature DB >> 28477301

Diagnostic value of MRI-based PSA density in predicting transperineal sector-guided prostate biopsy outcomes.

Findlay MacAskill1, Su-Min Lee2,3, David Eldred-Evans4, Wahyu Wulaningsih5, Rick Popert4,6, Konrad Wolfe7, Mieke Van Hemelrijck5, Giles Rottenberg6,8, Sidath H Liyanage9, Peter Acher1,6.   

Abstract

PURPOSE: Prostate-specific antigen (PSA) density (PSAD) has potential to increase the diagnostic utility of PSA, yet has had poor uptake in clinical practice. We aimed to determine the diagnostic value of magnetic resonance imaging-derived PSAD (MR-PSAD) in predicting transperineal sector-guided prostate biopsy (TPSB) outcomes.
MATERIALS AND METHODS: Men presenting for primary TPSB from 2007 to 2014 were considered. Histological outcomes were assessed and defined as: presence of any cancer or significant cancer defined as presence of Gleason 4 and/or maximum tumour core length (MCCL) ≥ 4 mm (G4); or Gleason 4 and/or MCCL ≥ 6 mm (G6). Sensitivity, specificity and positive and negative predictive values were calculated, and receiver operating characteristics (ROC) curves were generated to compare MR-PSAD and PSA.
RESULTS: Six hundred fifty-nine men were evaluated with mean age 62.5 ± 9 years, median PSA 6.7 ng/ml (range 0.5-40.0), prostate volume 40 cc (range 7-187) and MR-PSAD 0.15 ng/ml/cc (range 0.019-1.3). ROC area under the curve (95% CI) was significantly better for MR-PSAD than PSA for all cancer definitions (p < 0.001): 0.73 (0.70-0.76) versus 0.61 (0.57-0.64) for any cancer; 0.75 (0.71-0.78) versus 0.66 (0.62-0.69) for G4; and 0.77 (0.74-0.80) versus 0.68 (0.64-0.71) for G6. Sensitivities for MR-PSAD < 0.1 ng/ml/cc were 85.0, 89.9 and 91.9% for any, G4 and G6 cancer, respectively.
CONCLUSION: MR-PSAD may be better than total PSA in determining risk of positive biopsy outcome. Its use may improve risk stratification and reduce unnecessary biopsies.

Entities:  

Keywords:  Magnetic resonance imaging; Prostate biopsy; Prostate volume; Prostate-specific antigen; Prostatic neoplasms

Mesh:

Substances:

Year:  2017        PMID: 28477301     DOI: 10.1007/s11255-017-1609-8

Source DB:  PubMed          Journal:  Int Urol Nephrol        ISSN: 0301-1623            Impact factor:   2.370


  40 in total

1.  Interobserver variability of transrectal ultrasound for prostate volume measurement according to volume and observer experience.

Authors:  Young Jun Choi; Jeong Kon Kim; Hyun Jin Kim; Kyoung-Sik Cho
Journal:  AJR Am J Roentgenol       Date:  2009-02       Impact factor: 3.959

2.  Intra- and inter-observer variability and reliability of prostate volume measurement via two-dimensional and three-dimensional ultrasound imaging.

Authors:  S Tong; H N Cardinal; R F McLoughlin; D B Downey; A Fenster
Journal:  Ultrasound Med Biol       Date:  1998-06       Impact factor: 2.998

3.  The performance of prostate specific antigen, prostate specific antigen density and transition zone density in the era of extended biopsy schemes.

Authors:  Christopher S Elliott; Rajesh Shinghal; Joseph C Presti
Journal:  J Urol       Date:  2008-03-17       Impact factor: 7.450

4.  Prostate specific antigen density: a means of distinguishing benign prostatic hypertrophy and prostate cancer.

Authors:  M C Benson; I S Whang; A Pantuck; K Ring; S A Kaplan; C A Olsson; W H Cooner
Journal:  J Urol       Date:  1992-03       Impact factor: 7.450

5.  Combination of prostate imaging reporting and data system (PI-RADS) score and prostate-specific antigen (PSA) density predicts biopsy outcome in prostate biopsy naïve patients.

Authors:  Satoshi Washino; Tomohisa Okochi; Kimitoshi Saito; Tsuzumi Konishi; Masaru Hirai; Yutaka Kobayashi; Tomoaki Miyagawa
Journal:  BJU Int       Date:  2016-04-01       Impact factor: 5.588

6.  Impact of obesity on the predictive accuracy of prostate-specific antigen density and prostate-specific antigen in native Korean men undergoing prostate biopsy.

Authors:  Jae Heon Kim; Seung Whan Doo; Won Jae Yang; Kwang Woo Lee; Chang Ho Lee; Yun Seob Song; Yoon Su Jeon; Min Eui Kim; Soon-Sun Kwon
Journal:  Int J Urol       Date:  2014-05-13       Impact factor: 3.369

7.  Value of prostate specific antigen density and percent free prostate specific antigen for prostate cancer prognosis.

Authors:  Jonas Busch; Kristin Hamborg; Hellmuth-Alexander Meyer; John Buckendahl; Ahmed Magheli; Michael Lein; Klaus Jung; Kurt Miller; Carsten Stephan
Journal:  J Urol       Date:  2012-10-18       Impact factor: 7.450

8.  Multi-parametric magnetic resonance imaging to rule-in and rule-out clinically important prostate cancer in men at risk: a cohort study.

Authors:  Paul Rouse; Greg Shaw; Hashim U Ahmed; Alex Freeman; Clare Allen; Mark Emberton
Journal:  Urol Int       Date:  2011-06-22       Impact factor: 2.089

9.  Diagnostic accuracy of multi-parametric MRI and TRUS biopsy in prostate cancer (PROMIS): a paired validating confirmatory study.

Authors:  Hashim U Ahmed; Ahmed El-Shater Bosaily; Louise C Brown; Rhian Gabe; Richard Kaplan; Mahesh K Parmar; Yolanda Collaco-Moraes; Katie Ward; Richard G Hindley; Alex Freeman; Alex P Kirkham; Robert Oldroyd; Chris Parker; Mark Emberton
Journal:  Lancet       Date:  2017-01-20       Impact factor: 79.321

10.  ESUR prostate MR guidelines 2012.

Authors:  Jelle O Barentsz; Jonathan Richenberg; Richard Clements; Peter Choyke; Sadhna Verma; Geert Villeirs; Olivier Rouviere; Vibeke Logager; Jurgen J Fütterer
Journal:  Eur Radiol       Date:  2012-02-10       Impact factor: 5.315

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  4 in total

1.  Prospective Evaluation of 18F-DCFPyL PET/CT in Detection of High-Risk Localized Prostate Cancer: Comparison With mpMRI.

Authors:  Sonia Gaur; Esther Mena; Stephanie A Harmon; Maria L Lindenberg; Stephen Adler; Anita T Ton; Joanna H Shih; Sherif Mehralivand; Maria J Merino; Bradford J Wood; Peter A Pinto; Ronnie C Mease; Martin G Pomper; Peter L Choyke; Baris Turkbey
Journal:  AJR Am J Roentgenol       Date:  2020-07-08       Impact factor: 3.959

2.  The roles of MRI-based prostate volume and associated zone-adjusted prostate-specific antigen concentrations in predicting prostate cancer and high-risk prostate cancer.

Authors:  Song Zheng; Shaoqin Jiang; Zhenlin Chen; Zhangcheng Huang; Wenzhen Shi; Bingqiao Liu; Yue Xu; Yinan Guo; Huijie Yang; Mengqiang Li
Journal:  PLoS One       Date:  2019-11-19       Impact factor: 3.240

3.  A Nomogram Based on a Multiparametric Ultrasound Radiomics Model for Discrimination Between Malignant and Benign Prostate Lesions.

Authors:  Lei Liang; Xin Zhi; Ya Sun; Huarong Li; Jiajun Wang; Jingxu Xu; Jun Guo
Journal:  Front Oncol       Date:  2021-03-02       Impact factor: 6.244

4.  Radiomics prediction model for the improved diagnosis of clinically significant prostate cancer on biparametric MRI.

Authors:  Mengjuan Li; Tong Chen; Wenlu Zhao; Chaogang Wei; Xiaobo Li; Shaofeng Duan; Libiao Ji; Zhihua Lu; Junkang Shen
Journal:  Quant Imaging Med Surg       Date:  2020-02
  4 in total

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